Third-party emotion annotations, both human and LLM, show low to fair agreement with authors' self-reported emotions, with LLMs outperforming humans but still misaligning substantially.
Some Advances in Transformation-Based Part of Speech Tagging
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abstract
Most recent research in trainable part of speech taggers has explored stochastic tagging. While these taggers obtain high accuracy, linguistic information is captured indirectly, typically in tens of thousands of lexical and contextual probabilities. In [Brill92], a trainable rule-based tagger was described that obtained performance comparable to that of stochastic taggers, but captured relevant linguistic information in a small number of simple non-stochastic rules. In this paper, we describe a number of extensions to this rule-based tagger. First, we describe a method for expressing lexical relations in tagging that are not captured by stochastic taggers. Next, we show a rule-based approach to tagging unknown words. Finally, we show how the tagger can be extended into a k-best tagger, where multiple tags can be assigned to words in some cases of uncertainty.
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Can Third-parties Read Our Emotions?
Third-party emotion annotations, both human and LLM, show low to fair agreement with authors' self-reported emotions, with LLMs outperforming humans but still misaligning substantially.